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Hi. My name is Jeff Rzeszotarski.I am an Assistant Professor in the Information Science department at Cornell University with broad interests in data visualization, crowdsourcing, and social computing. My research focuses on helping both experts and everyday people make sense of complex data. I strongly believe that data big and small must be made accessible to as many people as possible, and I endeavor to encode that value into the systems I design and the research I pursue. I am currently looking for doctoral advisees interested in data visualization or crowdsourcing.

Research Projects

Naturalistic Visualizations

People are increasingly using mobile devices for everyday computing tasks, augmented by multi-touch interactions which break down the barrier between user and system through interactions that feel natural and match users' expectations. I develop data exploration techniques that use multi-touch and naturalistic interaction metaphors to closely match the sensemaking process users employ to make sense of complex data. By easily pivoting through dimensions, fluidly transitioning between views, and enabling direct manipulation of data, we can help users more deeply encode data relationships and identify outliers.

Crowd Observatory

Crowd labor markets such as Amazon Mechanical Turk, TaskRabbit and UpWork help employers to access large, instant-on pools of workers. However, the large scale of these markets limits their usefulness for complex or subjective tasks in which there is no single right answer. For example, there is no gold standard test question for tagging an image, and voting approaches don't work when workers write poems. In this project, my key insight is that the way workers work can often be as informative as workers' end products. Using a technique I call Task Fingerprinting, I apply machine learning to help stakeholders understand how and why their workflows are succeeding or failing.

Crowd Workflows

Crowd labor markets also hold the risk of overworking or fatiguing workers who must rapidly and accurately complete large amounts of tasks in short amounts of time. This poses risks to workers' health and wellbeing as well as task organizers' workflows. I explore introducing short, fun breaks into workflows to entertain crowdworkers as well as investigating how we might build systems that deliver the right task for the right worker at the right time, giving proper renumeration for effort and skill.

Collaborative Knowledge

Crowds of volunteers communicate and collaborate to build massive scale projects such as open source software, encyclopedias, and discussion forums. However, the very success of these systems generates with it a tremendous amount of historical data that pose a serious barrier to new contributors. For instance, past discussions and contributions to the Wikipedia article on Abortion amount to over 20 copies of Pride and Prejudice in length. Yet, in order to make successful contributions, newcomers must understand this information. I design novel visualizations powered by machine learning models that transform this historical data from a barrier into a beneficial resource for newcomers.

Friendsourcing

Asking public questions online is a complex decision for users, demanding a balance of social capital exchange and self-presentation maintenance. For example, a person may want to get health advice, but might avoid asking because they don't want to share their medical condition, or because they have recently asked their friends many other questions. Designers of question-asking services want to reduce these barriers, but lack a model of how users make this decision. I investigate the decisionmaking process people use on social networking services as they disclose and ask favors of others.

Publications

For a full list of publications and other professional details, please see my most recent CV.